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{{Short description|Software for population pharmacokinetic modeling}} |
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'''NONMEM''' is a [[non-linear mixed-effects modeling software]] package developed by Stuart L. Beal and Lewis B. Sheiner in the late 1970s at [[University of California, San Francisco]], and expanded by Robert Bauer at Icon PLC. Its name is an acronym for [[Nonlinear mixed-effects model|nonlinear mixed effects modeling]] but it is especially powerful in the context of [[population pharmacokinetics]], [[pharmacometrics]], and [[PK/PD models]].<ref name="PopPK1" /><ref name="PopPK2" /><ref name="PopPK3" /><ref name="PopPK4" /> |
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NONMEM models are written in NMTRAN, a dedicated [[Modeling language|model specification language]] that is translated into [[FORTRAN]], compiled on the fly and executed by a command-line script. Results are presented as text output files including tables.<ref name="BobTutor" /> There are multiple interfaces to assist modelers with housekeeping of files, tracking of model development, goodness-of-fit evaluations and graphical output, such as PsN and xpose<ref>{{Cite web |title=PsN :: Home |url=https://uupharmacometrics.github.io/PsN/ |access-date=2022-05-09 |website=uupharmacometrics.github.io}}</ref> and Wings for NONMEM.<ref>{{Cite web |title=Wings for NONMEM |url=http://wfn.sourceforge.net/ |access-date=2022-05-09 |website=wfn.sourceforge.net}}</ref> Current version for NONMEM is 7.5. |
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{{orphan|date=December 2007}} |
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== Model estimation == |
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'''NONMEM''' is a software package developed at [[UCSF]] for use in [[population pharmacokinetics]]. Its name is an acronym for non-linear mixed effects modeling. |
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NONMEM estimates its models according to principles of [[maximum likelihood estimation]]. [[nonlinear mixed-effects model]] generally do not have close-formed solutions, and therefore specific estimation methods are applied, such as linearization methods as first-order (FO), first-order conditional (FOCE) or the laplacian (LAPL), approximation methods such as iterative-two stage (ITS), importance sampling (IMP), stochastic approximation estimation (SAEM) or direct sampling.<ref name="TutorPart2" /> |
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==References== |
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<references> |
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<ref name="PopPK1">{{cite journal | title = Evaluation of Methods for Estimating Population Pharmacokinetic Parameters I. Michaelis-Menten Model: Routine Clinical Pharmacokinetic Data | journal = Journal of Pharmacokinetics and Biopharmaceutics | year = 1977 | first = Lewis B. | last = Sheiner |author2=B. Rosenberg |author3=V.V. Marathe | volume = 5 | issue = 5 | pages = 445–79 | doi=10.1007/BF01061728 | pmid=925881| s2cid = 28622472 }}</ref> |
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<ref name="PopPK2">{{cite journal | title = Evaluation of Methods for Estimating Population Pharmacokinetic Parameters I. Michaelis-Menten Model: Routine Clinical Pharmacokinetic Data | journal = Journal of Pharmacokinetics and Biopharmaceutics | year = 1980 | first = Lewis B. | last = Sheiner |author2=Stuart L. Beal | volume = 8 | issue = 6 | pages = 553–71 | doi=10.1007/BF01060053| pmid = 7229908 | s2cid = 31174590 }}</ref> |
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<ref name="PopPK3">{{cite journal | title = Evaluation of Methods for Estimating Population Pharmacokinetic Parameters II. Biexponential Model and Experimental Pharmacokinetic Data | journal = Journal of Pharmacokinetics and Biopharmaceutics | year = 1981 | first = Lewis B. | last = Sheiner |author2=Stuart L. Beal | volume = 9 | issue = 5 | pages = 635–51 | doi=10.1007/BF01061030| pmid = 7334463 | s2cid = 2668816 }}</ref> |
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<ref name="PopPK4">{{cite journal | title = Evaluation of Methods for Estimating Population Pharmacokinetic Parameters III. Monoexponential Model: Routine Clinical Pharmacokinetic Data | journal = Journal of Pharmacokinetics and Biopharmaceutics | year = 1983 | first = Lewis B. | last = Sheiner |author2=Stuart L. Beal | volume = 11 | issue = 3 | pages = 303–19 | doi=10.1007/BF01061870| pmid = 6644555 | s2cid = 9308691 }}</ref> |
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<ref name="BobTutor">{{Cite journal |last=Bauer |first=Robert J. |date=2019 |title=NONMEM Tutorial Part I: Description of Commands and Options, With Simple Examples of Population Analysis |journal=CPT: Pharmacometrics & Systems Pharmacology|language=en |volume=8 |issue=8 |pages=525–537 |doi=10.1002/psp4.12404 |issn=2163-8306 |pmc=6709426 |pmid=31056834}}</ref> |
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<ref name="TutorPart2">{{Cite journal |last=Bauer |first=Robert J. |date=2019 |title=NONMEM Tutorial Part II: Estimation Methods and Advanced Examples |journal=CPT: Pharmacometrics & Systems Pharmacology |language=en |volume=8 |issue=8 |pages=538–556 |doi=10.1002/psp4.12422 |issn=2163-8306 |pmc=6709422 |pmid=31044558}}</ref> |
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</references> |
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==External links== |
==External links== |
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*[https://www.iconplc.com/innovation/nonmem/ Product site] |
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[http://c255.ucsf.edu/nonmem1.html Brief Description of UCSF NONMEM] |
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*[https://www.mail-archive.com/nmusers@globomaxnm.com/maillist.html NONMEM UsersNet Archive] |
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[[Category:Numerical software]] |
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[[Category:Pharmacodynamics]] |
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[[Category:Pharmacokinetics]] |
[[Category:Pharmacokinetics]] |
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Latest revision as of 21:15, 21 November 2024
NONMEM is a non-linear mixed-effects modeling software package developed by Stuart L. Beal and Lewis B. Sheiner in the late 1970s at University of California, San Francisco, and expanded by Robert Bauer at Icon PLC. Its name is an acronym for nonlinear mixed effects modeling but it is especially powerful in the context of population pharmacokinetics, pharmacometrics, and PK/PD models.[1][2][3][4] NONMEM models are written in NMTRAN, a dedicated model specification language that is translated into FORTRAN, compiled on the fly and executed by a command-line script. Results are presented as text output files including tables.[5] There are multiple interfaces to assist modelers with housekeeping of files, tracking of model development, goodness-of-fit evaluations and graphical output, such as PsN and xpose[6] and Wings for NONMEM.[7] Current version for NONMEM is 7.5.
Model estimation
[edit]NONMEM estimates its models according to principles of maximum likelihood estimation. nonlinear mixed-effects model generally do not have close-formed solutions, and therefore specific estimation methods are applied, such as linearization methods as first-order (FO), first-order conditional (FOCE) or the laplacian (LAPL), approximation methods such as iterative-two stage (ITS), importance sampling (IMP), stochastic approximation estimation (SAEM) or direct sampling.[8]
References
[edit]- ^ Sheiner, Lewis B.; B. Rosenberg; V.V. Marathe (1977). "Evaluation of Methods for Estimating Population Pharmacokinetic Parameters I. Michaelis-Menten Model: Routine Clinical Pharmacokinetic Data". Journal of Pharmacokinetics and Biopharmaceutics. 5 (5): 445–79. doi:10.1007/BF01061728. PMID 925881. S2CID 28622472.
- ^ Sheiner, Lewis B.; Stuart L. Beal (1980). "Evaluation of Methods for Estimating Population Pharmacokinetic Parameters I. Michaelis-Menten Model: Routine Clinical Pharmacokinetic Data". Journal of Pharmacokinetics and Biopharmaceutics. 8 (6): 553–71. doi:10.1007/BF01060053. PMID 7229908. S2CID 31174590.
- ^ Sheiner, Lewis B.; Stuart L. Beal (1981). "Evaluation of Methods for Estimating Population Pharmacokinetic Parameters II. Biexponential Model and Experimental Pharmacokinetic Data". Journal of Pharmacokinetics and Biopharmaceutics. 9 (5): 635–51. doi:10.1007/BF01061030. PMID 7334463. S2CID 2668816.
- ^ Sheiner, Lewis B.; Stuart L. Beal (1983). "Evaluation of Methods for Estimating Population Pharmacokinetic Parameters III. Monoexponential Model: Routine Clinical Pharmacokinetic Data". Journal of Pharmacokinetics and Biopharmaceutics. 11 (3): 303–19. doi:10.1007/BF01061870. PMID 6644555. S2CID 9308691.
- ^ Bauer, Robert J. (2019). "NONMEM Tutorial Part I: Description of Commands and Options, With Simple Examples of Population Analysis". CPT: Pharmacometrics & Systems Pharmacology. 8 (8): 525–537. doi:10.1002/psp4.12404. ISSN 2163-8306. PMC 6709426. PMID 31056834.
- ^ "PsN :: Home". uupharmacometrics.github.io. Retrieved 2022-05-09.
- ^ "Wings for NONMEM". wfn.sourceforge.net. Retrieved 2022-05-09.
- ^ Bauer, Robert J. (2019). "NONMEM Tutorial Part II: Estimation Methods and Advanced Examples". CPT: Pharmacometrics & Systems Pharmacology. 8 (8): 538–556. doi:10.1002/psp4.12422. ISSN 2163-8306. PMC 6709422. PMID 31044558.
External links
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